Prognostic impact of blood urea nitrogen/creatinine ratio changes in patients with acute ischemic stroke

Association between the blood urea nitrogen-to-creatinine ratio and 3-month outcomes in patients with acute ischemic stroke: a secondary analysis based on a prospective cohort study

Introduction

This study aimed to assess the correlation between the blood urea nitrogen (BUN)-to-creatinine (BUN/Cr) ratio and adverse outcomes (AOs) at 3 months in patients with acute ischemic stroke (AIS) in the Korean population.

Methods

This cohort study encompassed 1906 cases of AIS at a South Korean hospital from January 2010 to December 2016. To determine the linear correlation between the BUN/Cr ratio and AOs in AIS, a binary logistic regression model (BLRM) was employed. Additionally, generalized additive models and techniques for smooth curve fitting were utilized to reveal the nonlinear dynamics between the BUN/Cr ratio and AOs in patients with AIS.

Results

The prevalence of AOs was 28.65%, with a median BUN/Cr ratio of 18.96. Following adjustments for covariates, the BLRM disclosed that the association between the BUN/Cr ratio and the risk of AOs in patients with AIS did not attain statistical significance. Nevertheless, a nonlinear relationship surfaced, pinpointing an inflection point at 21.591. To the left of this inflection point, a 31.42% reduction in the risk of AOs was noted for every 1-unit surge in the Z score of the BUN/Cr ratio [odds ratio (OR) = 0.686, 95% confidence interval (CI): 0.519, 0.906, p = 0.008]. On the right side of the inflection point, the effect size (OR = 1.405, 95% CI: 1.018, 1.902, p = 0.039) was determined.

Conclusion

The findings of this study underscore the intricate nature of the relationship between the BUN/Cr ratio and 3-month outcomes in patients with AIS, establishing a robust groundwork for future investigations.

Effect of Clemastine on Neurophysiological Outcomes in an Ovine Model of Neonatal Hypoxic-Ischemic Encephalopathy

Originally approved by the U.S. Food and Drug Administration (FDA) for its antihistamine properties, clemastine can also promote white matter integrity and has shown promise in the treatment of demyelinating diseases such as multiple sclerosis. Here, we conducted an in-depth analysis of the feasibility, safety, and neuroprotective efficacy of clemastine administration in near-term lambs (n = 25, 141-143 days) following a global ischemic insult induced via an umbilical cord occlusion (UCO) model. Lambs were randomly assigned to receive clemastine or placebo postnatally, and outcomes were assessed over a six-day period. Clemastine administration was well tolerated. While treated lambs demonstrated improvements in inflammatory scores, their neurodevelopmental outcomes were unchanged.

Interpretable machine learning for prediction of clinical outcomes in acute ischemic stroke

Background and aims

Predicting the prognosis of acute ischemic stroke (AIS) is crucial in a clinical setting for establishing suitable treatment plans. This study aimed to develop and validate a machine learning (ML) model that predicts the functional outcome of AIS patients and provides interpretable insights.

Methods

We included AIS patients from a multicenter stroke registry in this prognostic study. ML-based methods were utilized to predict 3-month functional outcomes, which were categorized as either favorable [modified Rankin Scale (mRS) ≤ 2] or unfavorable (mRS ≥ 3). The SHapley Additive exPlanations (SHAP) method was employed to identify significant features and interpret their contributions to the predictions of the model.

Results

The dataset comprised a derivation set of 3,687 patients and two external validation sets totaling 250 and 110 patients each. Among them, the number of unfavorable outcomes was 1,123 (30.4%) in the derivation set, and 93 (37.2%) and 32 (29.1%) in external sets A and B, respectively. Among the ML models used, the eXtreme Gradient Boosting model demonstrated the best performance. It achieved an area under the receiver operating characteristic curve (AUC-ROC) of 0.790 (95% CI: 0.775-0.806) on the internal test set and 0.791 (95% CI: 0.733-0.848) and 0.873 (95% CI: 0.798-0.948) on the two external test sets, respectively. The key features for predicting functional outcomes were the initial NIHSS, early neurologic deterioration (END), age, and white blood cell count. The END displayed noticeable interactions with several other features.

Conclusion

ML algorithms demonstrated proficient prediction for the 3-month functional outcome in AIS patients. With the aid of the SHAP method, we can attain an in-depth understanding of how critical features contribute to model predictions and how changes in these features influence such predictions.

Mean Platelet Volume/Platelet Count Ratio as a Predictor of Both Incidence and Prognosis of Acute Ischemic Stroke in Hemodialysis Patients

Purpose

This study aimed to explore whether the mean platelet volume/platelet count ratio (MPR) has predictive value for the occurrence and prognosis of acute ischemic stroke (AIS) in hemodialysis patients.

Patients and Methods

A total of 402 patients undergoing maintenance hemodialysis were screened and 259 were enrolled in this study. The receiver operating characteristic curve and area under the curve (AUC) were used to evaluate the predictive power of the models. The patients enrolled in this study were divided into three groups based on the tertiles of the MPR value (Q1, Q2, and Q3). Kaplan-Meier curves were used to investigate the association between the MPR and AIS-free survival in hemodialysis patients. Chi-square analysis was performed to explore the association between the MPR and AIS outcomes in hemodialysis patients. And the AIS outcome was assessed using the modified Rankin Scale (mRS).

Results

MPR had a predictive value for the occurrence of AIS (AUC=0.814) in hemodialysis patients with a high sensitivity and specificity. AIS-free survival rates in the MPR Q1, MPR Q2, and MPR Q3 groups were 0.930, 0.701, and 0.360, respectively. The proportion of patients with good outcomes (mRS 0-2) was significantly greater among patients in the MPR Q1-Q2 group than in the MPR Q3 group (0.844 vs 0.745, p <0.001).

Conclusion

The MPR can be used as a good predictor of AIS in patients undergoing hemodialysis. Patients on hemodialysis with increased MPR levels had a higher incidence of AIS and poorer functional outcomes than those with low MPR levels.

Relationship between the Blood Urea Nitrogen to Creatinine Ratio and In-Hospital Mortality in Non-Traumatic Subarachnoid Hemorrhage Patients: Based on Propensity Score Matching Method

(1) Background: To explore the correlation between the blood urea nitrogen to creatinine ratio (UCR) and in-hospital mortality in non-traumatic subarachnoid hemorrhage patients. (2) Methods: Specific clinical information was collected from the Medical Information Mart for Intensive Ⅳ (MIMIC-Ⅳ) database. The optimal cut-off value of the UCR was calculated with ROC curve analysis conducted using the maximum Youden index for the prediction of survival status. Univariable and multivariable logistic regression analyses were also carried out to assess the prognostic significance of UCR, and the Kaplan−Meier (K−M) analysis was conducted to draw the survival curves. Then, the 1:1 propensity score matching (PSM) method was applied to improve the reliability of the research results while balancing the unintended influence of underlying confounders. (3) Results: This retrospective cohort study included 961 patients. The optimal cut-off value of the UCR for in-hospital mortality was 27.208. The PSM was performed to identify 92 pairs of score-matched patients, with balanced differences exhibited for nearly all variables. According to the K−M analysis, those patients with a UCR of more than 27.208 showed a significantly higher level of in-hospital mortality compared to the patients with a UCR of less than 27.208 (p < 0.05). After the adjustment for possible confounders, those patients whose UCR was more than 27.208 still had a significantly higher level of in-hospital mortality than the patients whose UCR was less than 27.208, as revealed by the multivariable logistic regression analysis (OR = 3.783, 95% CI: 1.959~7.305, p < 0.001). Similarly, the in-hospital mortality remained substantially higher for those patients in the higher UCR group than for the patients in the lower UCR group after PSM. (4) Conclusion: A higher level of the UCR was evidently associated with an increased risk of in-hospital mortality, which made the ratio useful as a prognostic predictor of clinical outcomes for those patients with non-traumatic subarachnoid hemorrhage.